Journal: IEEE Transactions on Intelligent Transportation Systems

Abbreviation

IEEE trans. intell. transp. syst.

Publisher

IEEE

Journal Volumes

ISSN

1524-9050
1558-0016

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Publications1 - 10 of 54
  • Dynamic vehicle redistribution and online price incentives in shared mobility systems
    Item type: Journal Article
    Pfrommer, Julius; Warrington, Joseph; Schildbach, Georg; et al. (2014)
    IEEE Transactions on Intelligent Transportation Systems
  • Queue Estimation in a Connected Vehicle Environment
    Item type: Journal Article
    Yang, Kaidi; Menendez, Monica (2019)
    IEEE Transactions on Intelligent Transportation Systems
  • Network-wide freeway traffic estimation using sparse sensor data: A Dirichlet graph auto-encoder approach
    Item type: Journal Article
    Zhou, Qishen; Zhang, Yifan; Makridis, Michail; et al. (2025)
    IEEE Transactions on Intelligent Transportation Systems
    Network-wide Traffic State Estimation (TSE), which aims to infer a complete image of network traffic states with sparsely deployed sensors, plays a vital role in intelligent transportation systems. With the development of data-driven methods, traffic dynamics modeling has advanced significantly. However, TSE poses fundamental challenges for data-driven approaches, since historical patterns cannot be learned locally at sensor-free segments. Although graph representation learning shows promise in estimating states at locations without sensors, existing methods typically handle unobserved locations by filling them with zeros, introducing bias to the sensitive graph message propagation. The recently proposed Dirichlet Energy-based Feature Propagation (DEFP) method achieves State-Of-The-Art (SOTA) performance in unobserved node classification by eliminating the need for zero-filling. However, applying it to TSE faces three key challenges: inability to handle directed traffic networks, strong assumptions in traffic spatial correlation modeling, and overlooking distinct propagation rules of different patterns (e.g., congestion and free flow). We propose DGAE, a novel inductive graph representation model that addresses these challenges through theoretically derived DEFP for Directed graph (DEFP4D), enhanced spatial representation learning via DEFP4D-guided latent space encoding, and physics-guided propagation mechanisms that separately handle congested and free-flow patterns. Experiments on three traffic datasets demonstrate that DGAE outperforms existing SOTA methods and exhibits strong cross-city transferability. Furthermore, DEFP4D can serve as a standalone lightweight solution, showing superior performance under extremely sparse sensor conditions. The code of this work is publicly available at: https://github.com/ZJU-TSELab/DGAE
  • Integrated Urban Traffic Control for the Reduction of Travel Delays and Emissions
    Item type: Journal Article
    Lin, Shu; De Schutter, Bart; Xi, Yugeng; et al. (2013)
    IEEE Transactions on Intelligent Transportation Systems
  • Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control
    Item type: Journal Article
    Du, Guodong; Zou, Yuan; Zhang, Xudong; et al. (2023)
    IEEE Transactions on Intelligent Transportation Systems
    The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.
  • The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring
    Item type: Journal Article
    Janecek, Andreas; Valerio, Danilo; Hummel, Karin A.; et al. (2015)
    IEEE Transactions on Intelligent Transportation Systems
  • HeightFormer: Learning Height Prediction in Voxel Features for Roadside Vision Centric 3D Object Detection via Transformer
    Item type: Journal Article
    Zhang, Zhang; Sun, Chao; Yue, Chao; et al. (2026)
    IEEE Transactions on Intelligent Transportation Systems
    Roadside vision centric 3D object detection has received increasing attention in recent years. It expands the perception range of autonomous vehicles, enhances the road safety. Previous methods focused on predicting per-pixel height rather than depth, making significant gains in roadside visual perception. While it is limited by the perspective property of near-large and far-small on image features, making it difficult for network to understand real dimension of objects in the 3D world. Bird’s Eye View (BEV) features and voxel features present the real distribution of objects in 3D world compared to the image features. However, BEV features tend to lose details due to the lack of explicit height information, and voxel features are computationally expensive. Inspired by this insight, an efficient framework learning height prediction in voxel features via transformer is proposed, dubbed HeightFormer. It groups the voxel features into local height sequences, and utilize attention mechanism to obtain height distribution prediction. Subsequently, the local height sequences are reassembled to generate accurate voxel features. The proposed method is applied to two large-scale roadside benchmarks, DAIR-V2X-I and Rope3D. Extensive experiments are performed and the HeightFormer outperforms the state-of-the-art methods in roadside vision centric 3D object detection task. Code will be at https://github.com/zhangzhang2024/HeightFormer
  • Fitting empirical fundamental diagrams of road traffic
    Item type: Review Article
    Bramich, Daniel M.; Menendez, Monica; Ambühl, Lukas (2022)
    IEEE Transactions on Intelligent Transportation Systems
    Understanding the inter-relationships between traffic flow, density, and speed through the study of the fundamental diagram of road traffic is critical for traffic modelling and management. Consequently, over the last 85 years, a wealth of models have been developed for its functional form. However, there has been no clear answer as to which model is the most appropriate for observed (i.e. empirical) fundamental diagrams and under which conditions. A lack of data has been partly to blame. Motivated by shortcomings in previous reviews, we first present a comprehensive literature review on modelling the functional form of empirical fundamental diagrams. We then perform fits of 50 previously proposed models to a high quality sample of 10,150 empirical fundamental diagrams pertaining to 25 cities. Comparing the fits using information criteria, we find that the non-parametric Sun model greatly outperforms all of the other models. The Sun model maintains its winning position regardless of road type and congestion level. Our study, the first of its kind when considering the number of models tested and the amount of data used, finally provides a definitive answer to the question ``Which model for the functional form of an empirical fundamental diagram is currently the best?''. The word ``currently'' in this question is key, because previously proposed models adopt an inappropriate Gaussian noise model with constant variance. We advocate that future research should shift focus to exploring more sophisticated noise models. This will lead to an improved understanding of empirical fundamental diagrams and their underlying functional forms.
  • Perimeter control and route guidance of multi-region MFD systems with boundary queues using colored Petri Nets
    Item type: Journal Article
    Fu, Hui; Chen, Saifei; Chen, Kaiyu; et al. (2022)
    IEEE Transactions on Intelligent Transportation Systems
    Perimeter control based on Macroscopic Fundamental Diagram (MFD) aims to meter the number of transferring vehicles at the periphery of the protected urban region in order to obtain the desired number of vehicles in that region. The advantage of perimeter control is less computational effort, while its drawback is that it may create long queues and delays at the perimeter of the controlled area. For capturing boundary queue dynamics, an enhanced accumulation-based MFD model is proposed using colored Petri Nets by considering transfer flows, boundary queues and travel delays simultaneously. The gated intersections and related road segments on the border of a protected region are modeled as so-called boundary buffers. Based on the enhanced MFD model, anintegrated perimeter control framework is proposed with consideration of travel time and queuing time in buffers. In this framework, the controllers between peripheral and protected region are optimized using model predictive control theory. Then, internal flow controllers are adopted to homogenize traffic density among subregions, and route guidance is also used to balance the number of queuing vehicles among boundary buffers. Simulation results verify the effectiveness of the proposed integrated perimeter control. Furthermore, the impacts of buffer storage capacity on region heterogeneity and trip completion rates are also investigated in this paper.
  • Modelling, relocation and real-time inventory control of one-way electric cars sharing systems in a stochastic Petri nets framework
    Item type: Journal Article
    Benarbia, Taha; Axhausen, Kay W.; Farooq, Bilal (2021)
    IEEE Transactions on Intelligent Transportation Systems
    In recent years, one-way Electric Car sharing (ECs) systems have been introduced in many cities. One-way trips, as well as battery range issues, directly influence the quality and dynamics of such systems. Due to the demand and supply imbalance at stations, the ECs operators are faced with crucial operational challenges to reduce the relocation costs and increase the number of users. An agent-based relocation strategy based on real-time inventory control within the framework of generalized stochastic Petri Nets (PN) and a discrete event simulation has been proposed in this paper. Furthermore, an associated system performance evaluation was also developed. This model further assesses the effects of system characteristics such as the battery charging level availability threshold on the behavior and dynamics of the system. Moreover, the developed model and simulation show the potential of using PN models to predict critical situations, analyze relocation strategy efficiency, and improve system performance. Results from the simulation indicate that the overall relocation trips are reduced by estimating the time to launch the relocation process, as well as the conflicts between agents (controlling the assignment of agents among stations) during the balancing process are efficiently resolved. The proposed model and simulation algorithm have been applied to the BlueSG network in downtown Singapore.
Publications1 - 10 of 54